{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5498da6-85a6-4a21-b164-84af87f658a3",
   "metadata": {},
   "source": [
    "### 需求分析\n",
    "1. 用户个体消费分析\n",
    "2. 用户整体消费分析（按月）\n",
    "3. 用户消费行为分析\n",
    "4. 用户分层\n",
    "5. 新老、活跃、回流用户分析\n",
    "6. 用户的购买周期\n",
    "7. 用户生命周期\n",
    "8. 复购率、回购率分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1f62b953-9189-4d8f-9a9e-f0d4827a6769",
   "metadata": {},
   "outputs": [],
   "source": [
    "#引包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot') # 更改绘图风格，R语言绘图库\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "97818f4d-51c7-4825-a1fb-cc6e773a25e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>product</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19970101</td>\n",
       "      <td>1</td>\n",
       "      <td>11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>1</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>5</td>\n",
       "      <td>77.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>19970102</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>19970330</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  order_dt  product  amount\n",
       "0        1  19970101        1   11.77\n",
       "1        2  19970112        1   12.00\n",
       "2        2  19970112        5   77.00\n",
       "3        3  19970102        2   20.76\n",
       "4        3  19970330        2   20.76"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据引入  user_id  用户id  order_dt 订单时间  order_products 购买数量 order_amount 购买金额\n",
    "column = ['user_id','order_dt','product','amount']\n",
    "df = pd.read_table('data/CDNOW_master.txt',names=column,sep=r'\\s+') # sep:'\\s+ 匹配任意空格'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "db21af57-9d23-487f-b576-106289a56b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>product</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>69659.000000</td>\n",
       "      <td>6.965900e+04</td>\n",
       "      <td>69659.000000</td>\n",
       "      <td>69659.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11470.854592</td>\n",
       "      <td>1.997228e+07</td>\n",
       "      <td>2.410040</td>\n",
       "      <td>35.893648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6819.904848</td>\n",
       "      <td>3.837735e+03</td>\n",
       "      <td>2.333924</td>\n",
       "      <td>36.281942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.997010e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5506.000000</td>\n",
       "      <td>1.997022e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11410.000000</td>\n",
       "      <td>1.997042e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17273.000000</td>\n",
       "      <td>1.997111e+07</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>43.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23570.000000</td>\n",
       "      <td>1.998063e+07</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1286.010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id      order_dt       product        amount\n",
       "count  69659.000000  6.965900e+04  69659.000000  69659.000000\n",
       "mean   11470.854592  1.997228e+07      2.410040     35.893648\n",
       "std     6819.904848  3.837735e+03      2.333924     36.281942\n",
       "min        1.000000  1.997010e+07      1.000000      0.000000\n",
       "25%     5506.000000  1.997022e+07      1.000000     14.490000\n",
       "50%    11410.000000  1.997042e+07      2.000000     25.980000\n",
       "75%    17273.000000  1.997111e+07      3.000000     43.700000\n",
       "max    23570.000000  1.998063e+07     99.000000   1286.010000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d4768a68-cc5c-4156-9feb-f871d883348b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 69659 entries, 0 to 69658\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   user_id   69659 non-null  int64  \n",
      " 1   order_dt  69659 non-null  int64  \n",
      " 2   product   69659 non-null  int64  \n",
      " 3   amount    69659 non-null  float64\n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 2.1 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "29c85f2c-dd57-4323-be40-3160f5e0769a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 69659 entries, 0 to 69658\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   user_id   69659 non-null  int64         \n",
      " 1   order_dt  69659 non-null  int64         \n",
      " 2   product   69659 non-null  int64         \n",
      " 3   amount    69659 non-null  float64       \n",
      " 4   date      69659 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(3)\n",
      "memory usage: 2.7 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>product</th>\n",
       "      <th>amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19970101</td>\n",
       "      <td>1</td>\n",
       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>1</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>5</td>\n",
       "      <td>77.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>19970102</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>1997-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>19970330</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>1997-03-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  order_dt  product  amount       date\n",
       "0        1  19970101        1   11.77 1997-01-01\n",
       "1        2  19970112        1   12.00 1997-01-12\n",
       "2        2  19970112        5   77.00 1997-01-12\n",
       "3        3  19970102        2   20.76 1997-01-02\n",
       "4        3  19970330        2   20.76 1997-03-30"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['order_dt'],format='%Y%m%d')\n",
    "print(df.info())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "583260c0-aa6d-4795-b959-029430edcb0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 69659 entries, 0 to 69658\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   user_id     69659 non-null  int64         \n",
      " 1   order_dt    69659 non-null  int64         \n",
      " 2   product     69659 non-null  int64         \n",
      " 3   amount      69659 non-null  float64       \n",
      " 4   date        69659 non-null  datetime64[ns]\n",
      " 5   month       69659 non-null  int32         \n",
      " 6   year_month  69659 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(1), int32(1), int64(3)\n",
      "memory usage: 3.5 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>product</th>\n",
       "      <th>amount</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year_month</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19970101</td>\n",
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       "      <td>1997-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>1</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>5</td>\n",
       "      <td>77.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>19970102</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>1997-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>19970330</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>1997-03-30</td>\n",
       "      <td>3</td>\n",
       "      <td>1997-03-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  order_dt  product  amount       date  month year_month\n",
       "0        1  19970101        1   11.77 1997-01-01      1 1997-01-01\n",
       "1        2  19970112        1   12.00 1997-01-12      1 1997-01-01\n",
       "2        2  19970112        5   77.00 1997-01-12      1 1997-01-01\n",
       "3        3  19970102        2   20.76 1997-01-02      1 1997-01-01\n",
       "4        3  19970330        2   20.76 1997-03-30      3 1997-03-01"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month'] = df['date'].dt.month\n",
    "df['year_month'] = df['date'].dt.to_period('M').dt.start_time\n",
    "print(df.info())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2c9f9109-a57d-4e9a-af00-4c4b4d12aef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按月份统计产品购买数量、金额、次数、人数\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(221)\n",
    "gp_month = df.groupby(by=\"year_month\")['product'].sum().plot()\n",
    "plt.title('Product purchase quantity in month')\n",
    "plt.subplot(222)\n",
    "gp_month = df.groupby(by=\"year_month\")['amount'].sum().plot()\n",
    "plt.title('amounts in month')\n",
    "plt.subplot(223)\n",
    "gp_month = df.groupby(by=\"year_month\")['user_id'].count().plot()\n",
    "plt.title('number of orders')\n",
    "plt.subplot(224)\n",
    "#gp_month = df.groupby(by=\"year_month\")['user_id'].apply(lambda x:len(x.drop_duplicates())).plot()  # 方案一 使用lambda表达式\n",
    "gp_month = df.groupby(by=\"year_month\")['user_id'].nunique().plot()\n",
    "plt.title('Monthly Consumer Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0f2ef76c-060b-4f95-9540-9aac7024d894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "商品平均单价 15.501922553255737\n",
      "             amount       product  average_amount\n",
      "count  23570.000000  23570.000000    23570.000000\n",
      "mean     106.080426      7.122656       15.501923\n",
      "std      240.925195     16.983531        8.006495\n",
      "min        0.000000      1.000000        0.000000\n",
      "25%       19.970000      1.000000       12.665000\n",
      "50%       43.395000      3.000000       14.262500\n",
      "75%      106.475000      7.000000       15.960000\n",
      "max    13990.930000   1033.000000      283.970000\n",
      "用户数量 23570\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "      <th>product</th>\n",
       "      <th>average_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.77</td>\n",
       "      <td>1</td>\n",
       "      <td>11.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.00</td>\n",
       "      <td>6</td>\n",
       "      <td>14.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.46</td>\n",
       "      <td>16</td>\n",
       "      <td>9.778750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.50</td>\n",
       "      <td>7</td>\n",
       "      <td>14.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>385.61</td>\n",
       "      <td>29</td>\n",
       "      <td>13.296897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>36.00</td>\n",
       "      <td>2</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>20.97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>121.70</td>\n",
       "      <td>6</td>\n",
       "      <td>20.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>25.74</td>\n",
       "      <td>2</td>\n",
       "      <td>12.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>94.08</td>\n",
       "      <td>5</td>\n",
       "      <td>18.816000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         amount  product  average_amount\n",
       "user_id                                 \n",
       "1         11.77        1       11.770000\n",
       "2         89.00        6       14.833333\n",
       "3        156.46       16        9.778750\n",
       "4        100.50        7       14.357143\n",
       "5        385.61       29       13.296897\n",
       "...         ...      ...             ...\n",
       "23566     36.00        2       18.000000\n",
       "23567     20.97        1       20.970000\n",
       "23568    121.70        6       20.283333\n",
       "23569     25.74        2       12.870000\n",
       "23570     94.08        5       18.816000\n",
       "\n",
       "[23570 rows x 3 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户个体消费分析\n",
    "\n",
    "gp_user = df.groupby(by='user_id')[['amount','product']].sum()\n",
    "gp_user['average_amount'] = gp_user['amount']/gp_user['product']\n",
    "print('商品平均单价',gp_user['average_amount'].mean())\n",
    "print(gp_user.describe())\n",
    "print('用户数量',len(gp_user))\n",
    "gp_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "665765ac-5050-4267-85b4-d0d819366cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制产品购买数量与金额的散点图(可以看出用户购买商品的平均单价)\n",
    "\n",
    "df.plot(kind='scatter',x='product',y='amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "32fb4e0b-4e3c-4f2d-8d0e-858c91b28b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用户消费分布\n",
    "\n",
    "fig,axes = plt.subplots(1,2,figsize=(12,4))\n",
    "\n",
    "\n",
    "df['amount'].plot(kind='hist',bins=50,ax=axes[0])  #bins:区间分数，影响柱子的宽度，值越大柱子越细\n",
    "axes[0].set_xlabel('The consumption amount of each order')\n",
    "\n",
    "gp_user['product'].plot(kind='hist',bins=50,ax=axes[1])\n",
    "axes[1].set_xlabel('The purchase quantity of each user')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()  #自动调节子图间距\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "97c2df4a-84be-487a-939a-032ffd4a07da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>product</th>\n",
       "      <th>average_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16921</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>713</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9835</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7005</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23565</th>\n",
       "      <td>7931</td>\n",
       "      <td>6497.18</td>\n",
       "      <td>514</td>\n",
       "      <td>12.640428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>19339</td>\n",
       "      <td>6552.70</td>\n",
       "      <td>378</td>\n",
       "      <td>17.335185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>7983</td>\n",
       "      <td>6973.07</td>\n",
       "      <td>536</td>\n",
       "      <td>13.009459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>14048</td>\n",
       "      <td>8976.33</td>\n",
       "      <td>1033</td>\n",
       "      <td>8.689574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>7592</td>\n",
       "      <td>13990.93</td>\n",
       "      <td>917</td>\n",
       "      <td>15.257285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id    amount  product  average_amount\n",
       "0        16921      0.00        1        0.000000\n",
       "1          713      0.00        1        0.000000\n",
       "2         9835      0.00        1        0.000000\n",
       "3         7005      0.00        1        0.000000\n",
       "4         1948      0.00        1        0.000000\n",
       "...        ...       ...      ...             ...\n",
       "23565     7931   6497.18      514       12.640428\n",
       "23566    19339   6552.70      378       17.335185\n",
       "23567     7983   6973.07      536       13.009459\n",
       "23568    14048   8976.33     1033        8.689574\n",
       "23569     7592  13990.93      917       15.257285\n",
       "\n",
       "[23570 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户累计消费金额占比分析（用户贡献度）\n",
    "\n",
    "gp_user = gp_user.sort_values(by='amount').reset_index()\n",
    "gp_user "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0e3fadad-a4fb-4dec-bed9-032b954969e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>product</th>\n",
       "      <th>average_amount</th>\n",
       "      <th>cum_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23565</th>\n",
       "      <td>7931</td>\n",
       "      <td>6497.18</td>\n",
       "      <td>514</td>\n",
       "      <td>12.640428</td>\n",
       "      <td>2463822.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>19339</td>\n",
       "      <td>6552.70</td>\n",
       "      <td>378</td>\n",
       "      <td>17.335185</td>\n",
       "      <td>2470375.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>7983</td>\n",
       "      <td>6973.07</td>\n",
       "      <td>536</td>\n",
       "      <td>13.009459</td>\n",
       "      <td>2477348.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>14048</td>\n",
       "      <td>8976.33</td>\n",
       "      <td>1033</td>\n",
       "      <td>8.689574</td>\n",
       "      <td>2486324.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>7592</td>\n",
       "      <td>13990.93</td>\n",
       "      <td>917</td>\n",
       "      <td>15.257285</td>\n",
       "      <td>2500315.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id    amount  product  average_amount  cum_amount\n",
       "23565     7931   6497.18      514       12.640428  2463822.60\n",
       "23566    19339   6552.70      378       17.335185  2470375.30\n",
       "23567     7983   6973.07      536       13.009459  2477348.37\n",
       "23568    14048   8976.33     1033        8.689574  2486324.70\n",
       "23569     7592  13990.93      917       15.257285  2500315.63"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每个用户消费金额累加\n",
    "gp_user['cum_amount'] = np.cumsum(gp_user['amount'])\n",
    "gp_user.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "85873ec2-71a4-4940-8909-bb62f9914cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2500315.63)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#消费金额总值\n",
    "total_amount = gp_user['amount'].sum()\n",
    "total_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e8ac3516-b8a6-4a99-beb5-66cbe3ae2070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>product</th>\n",
       "      <th>average_amount</th>\n",
       "      <th>cum_amount</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16921</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>713</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9835</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7005</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23565</th>\n",
       "      <td>7931</td>\n",
       "      <td>6497.18</td>\n",
       "      <td>514</td>\n",
       "      <td>12.640428</td>\n",
       "      <td>2463822.60</td>\n",
       "      <td>0.985405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>19339</td>\n",
       "      <td>6552.70</td>\n",
       "      <td>378</td>\n",
       "      <td>17.335185</td>\n",
       "      <td>2470375.30</td>\n",
       "      <td>0.988025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>7983</td>\n",
       "      <td>6973.07</td>\n",
       "      <td>536</td>\n",
       "      <td>13.009459</td>\n",
       "      <td>2477348.37</td>\n",
       "      <td>0.990814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>14048</td>\n",
       "      <td>8976.33</td>\n",
       "      <td>1033</td>\n",
       "      <td>8.689574</td>\n",
       "      <td>2486324.70</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>7592</td>\n",
       "      <td>13990.93</td>\n",
       "      <td>917</td>\n",
       "      <td>15.257285</td>\n",
       "      <td>2500315.63</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id    amount  product  average_amount  cum_amount      prop\n",
       "0        16921      0.00        1        0.000000        0.00  0.000000\n",
       "1          713      0.00        1        0.000000        0.00  0.000000\n",
       "2         9835      0.00        1        0.000000        0.00  0.000000\n",
       "3         7005      0.00        1        0.000000        0.00  0.000000\n",
       "4         1948      0.00        1        0.000000        0.00  0.000000\n",
       "...        ...       ...      ...             ...         ...       ...\n",
       "23565     7931   6497.18      514       12.640428  2463822.60  0.985405\n",
       "23566    19339   6552.70      378       17.335185  2470375.30  0.988025\n",
       "23567     7983   6973.07      536       13.009459  2477348.37  0.990814\n",
       "23568    14048   8976.33     1033        8.689574  2486324.70  0.994404\n",
       "23569     7592  13990.93      917       15.257285  2500315.63  1.000000\n",
       "\n",
       "[23570 rows x 6 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_user['prop'] = gp_user.apply(lambda x:x['cum_amount'] / total_amount,axis=1)\n",
    "#gp_user['prop'] = gp_user['cum_amount'] / total_amount\n",
    "gp_user\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "86244c4f-25a3-4950-9ac0-0339ab0eb5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp_user['prop'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7103fcfa-3214-4ff1-9158-7beb97f2acea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date  count\n",
      "82 1997-01-01    209\n",
      "68 1997-01-02    241\n",
      "73 1997-01-03    228\n",
      "83 1997-01-04    174\n",
      "64 1997-01-05    250\n",
      "..        ...    ...\n",
      "79 1997-03-21    213\n",
      "65 1997-03-22    250\n",
      "49 1997-03-23    277\n",
      "57 1997-03-24    263\n",
      "69 1997-03-25    241\n",
      "\n",
      "[84 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用户消费行为\n",
    "#首购时间\n",
    "print(df.groupby(by=\"user_id\")['date'].min().value_counts().reset_index().sort_values(by=\"date\"))  #调整为按日期排序\n",
    "df.groupby(by=\"user_id\")['date'].min().value_counts().reset_index().sort_values(by=\"date\").plot(x=\"date\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7e003f25-a901-4d4e-9e66-adf7b380d589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#最后一次购买时间\n",
    "df.groupby(by=\"user_id\")['date'].max().value_counts().reset_index().sort_values(by=\"date\").plot(x=\"date\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cf2590d8-cb17-4990-b427-72166f2ebb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "      <th>date</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.46</td>\n",
       "      <td>1998-05-28</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.50</td>\n",
       "      <td>1997-12-12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>385.61</td>\n",
       "      <td>1998-01-03</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>36.00</td>\n",
       "      <td>1997-03-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>20.97</td>\n",
       "      <td>1997-03-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>121.70</td>\n",
       "      <td>1997-04-22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>25.74</td>\n",
       "      <td>1997-03-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>94.08</td>\n",
       "      <td>1997-03-26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         amount       date  product\n",
       "user_id                            \n",
       "1         11.77 1997-01-01        1\n",
       "2         89.00 1997-01-12        2\n",
       "3        156.46 1998-05-28        6\n",
       "4        100.50 1997-12-12        4\n",
       "5        385.61 1998-01-03       11\n",
       "...         ...        ...      ...\n",
       "23566     36.00 1997-03-25        1\n",
       "23567     20.97 1997-03-25        1\n",
       "23568    121.70 1997-04-22        3\n",
       "23569     25.74 1997-03-25        1\n",
       "23570     94.08 1997-03-26        2\n",
       "\n",
       "[23570 rows x 3 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户分层，构建RFM模型\n",
    "\n",
    "rfm = df.pivot_table(index='user_id',\n",
    "                     values=['product','amount','date'],\n",
    "                     aggfunc={\n",
    "                            'date':'max',   #最后一次购买\n",
    "                            'product':'count',  #购买产品频次\n",
    "                            'amount':'sum'  #消费总金额\n",
    "                            })\n",
    "\n",
    "rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f790c444-9ba0-4d9b-b209-7fca3d6b3be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "      <th>date</th>\n",
       "      <th>F</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>2</td>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.46</td>\n",
       "      <td>1998-05-28</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.50</td>\n",
       "      <td>1997-12-12</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>385.61</td>\n",
       "      <td>1998-01-03</td>\n",
       "      <td>11</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M       date   F    R\n",
       "user_id                            \n",
       "1         11.77 1997-01-01   1  545\n",
       "2         89.00 1997-01-12   2  534\n",
       "3        156.46 1998-05-28   6   33\n",
       "4        100.50 1997-12-12   4  200\n",
       "5        385.61 1998-01-03  11  178"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rfm['R'] = -(rfm['date'] - rfm['date'].max())/np.timedelta64(1,'D')  #取相差的天数，保留一位小数\n",
    "rfm['R'] = -(rfm['date'] - rfm['date'].max()).dt.days\n",
    "rfm.rename(columns={'product':'F','amount':'M'},inplace=True)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "745156f3-80c9-4ba9-89c6-07bcffb994f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "      <th>date</th>\n",
       "      <th>F</th>\n",
       "      <th>R</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>545</td>\n",
       "      <td>一般发展客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>2</td>\n",
       "      <td>534</td>\n",
       "      <td>一般发展客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.46</td>\n",
       "      <td>1998-05-28</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.50</td>\n",
       "      <td>1997-12-12</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>一般保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>385.61</td>\n",
       "      <td>1998-01-03</td>\n",
       "      <td>11</td>\n",
       "      <td>178</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M       date   F    R   label\n",
       "user_id                                    \n",
       "1         11.77 1997-01-01   1  545  一般发展客户\n",
       "2         89.00 1997-01-12   2  534  一般发展客户\n",
       "3        156.46 1998-05-28   6   33  重要保持客户\n",
       "4        100.50 1997-12-12   4  200  一般保持客户\n",
       "5        385.61 1998-01-03  11  178  重要保持客户"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rfm['R'] - rfm['R'].mean()\n",
    "\n",
    "\n",
    "def rfm_func(x):\n",
    "   level = x.apply(lambda x:'1' if x>=1 else '0')\n",
    "   label = level['R']+level['F']+level['M']\n",
    "   d={\n",
    "       '111':'重要价值客户',\n",
    "       '011':'重要保持客户',\n",
    "       '101':'重要发展客户',\n",
    "       '001':'重要挽留客户',\n",
    "       '110':'一般价值客户',\n",
    "       '010':'一般保持客户',\n",
    "       '100':'一般发展客户',\n",
    "       '000':'一般挽留客户',\n",
    "   }\n",
    "   result = d[label]   \n",
    "   return result\n",
    "\n",
    "rfm['label'] = rfm[['R','F','M']].apply(lambda x:x - x.mean()).apply(rfm_func,axis=1)\n",
    "\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6bd0dff4-aecd-4b61-855b-d732e35ae1fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label,gp in rfm.groupby(by='label'):\n",
    "   # print(label,gp)\n",
    "    x=gp['F']\n",
    "    y=gp['R']\n",
    "    plt.scatter(x,y,label=label)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('F')\n",
    "plt.ylabel('R')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d72cff4b-3464-4edc-84f6-04f3f8def1a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       user_id  order_dt  product  amount       date  month year_month\n",
      "0            1  19970101        1   11.77 1997-01-01      1 1997-01-01\n",
      "1            2  19970112        1   12.00 1997-01-12      1 1997-01-01\n",
      "2            2  19970112        5   77.00 1997-01-12      1 1997-01-01\n",
      "3            3  19970102        2   20.76 1997-01-02      1 1997-01-01\n",
      "4            3  19970330        2   20.76 1997-03-30      3 1997-03-01\n",
      "...        ...       ...      ...     ...        ...    ...        ...\n",
      "69654    23568  19970405        4   83.74 1997-04-05      4 1997-04-01\n",
      "69655    23568  19970422        1   14.99 1997-04-22      4 1997-04-01\n",
      "69656    23569  19970325        2   25.74 1997-03-25      3 1997-03-01\n",
      "69657    23570  19970325        3   51.12 1997-03-25      3 1997-03-01\n",
      "69658    23570  19970326        2   42.96 1997-03-26      3 1997-03-01\n",
      "\n",
      "[69659 rows x 7 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1997-02-01</th>\n",
       "      <th>1997-03-01</th>\n",
       "      <th>1997-04-01</th>\n",
       "      <th>1997-05-01</th>\n",
       "      <th>1997-06-01</th>\n",
       "      <th>1997-07-01</th>\n",
       "      <th>1997-08-01</th>\n",
       "      <th>1997-09-01</th>\n",
       "      <th>1997-10-01</th>\n",
       "      <th>1997-11-01</th>\n",
       "      <th>1997-12-01</th>\n",
       "      <th>1998-01-01</th>\n",
       "      <th>1998-02-01</th>\n",
       "      <th>1998-03-01</th>\n",
       "      <th>1998-04-01</th>\n",
       "      <th>1998-05-01</th>\n",
       "      <th>1998-06-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "user_id                                                                  \n",
       "1                  1.0         0.0         0.0         0.0         0.0   \n",
       "2                  2.0         0.0         0.0         0.0         0.0   \n",
       "3                  1.0         0.0         1.0         1.0         0.0   \n",
       "4                  2.0         0.0         0.0         0.0         0.0   \n",
       "5                  2.0         1.0         0.0         1.0         1.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              0.0         0.0         1.0         0.0         0.0   \n",
       "23567              0.0         0.0         1.0         0.0         0.0   \n",
       "23568              0.0         0.0         1.0         2.0         0.0   \n",
       "23569              0.0         0.0         1.0         0.0         0.0   \n",
       "23570              0.0         0.0         2.0         0.0         0.0   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "user_id                                                                  \n",
       "1                  0.0         0.0         0.0         0.0         0.0   \n",
       "2                  0.0         0.0         0.0         0.0         0.0   \n",
       "3                  0.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         0.0         1.0         0.0         0.0   \n",
       "5                  1.0         1.0         0.0         1.0         0.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              0.0         0.0         0.0         0.0         0.0   \n",
       "23567              0.0         0.0         0.0         0.0         0.0   \n",
       "23568              0.0         0.0         0.0         0.0         0.0   \n",
       "23569              0.0         0.0         0.0         0.0         0.0   \n",
       "23570              0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "user_id                                                                  \n",
       "1                  0.0         0.0         0.0         0.0         0.0   \n",
       "2                  0.0         0.0         0.0         0.0         0.0   \n",
       "3                  2.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         1.0         0.0         0.0         0.0   \n",
       "5                  0.0         2.0         1.0         0.0         0.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              0.0         0.0         0.0         0.0         0.0   \n",
       "23567              0.0         0.0         0.0         0.0         0.0   \n",
       "23568              0.0         0.0         0.0         0.0         0.0   \n",
       "23569              0.0         0.0         0.0         0.0         0.0   \n",
       "23570              0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "user_id                                         \n",
       "1                  0.0         0.0         0.0  \n",
       "2                  0.0         0.0         0.0  \n",
       "3                  0.0         1.0         0.0  \n",
       "4                  0.0         0.0         0.0  \n",
       "5                  0.0         0.0         0.0  \n",
       "...                ...         ...         ...  \n",
       "23566              0.0         0.0         0.0  \n",
       "23567              0.0         0.0         0.0  \n",
       "23568              0.0         0.0         0.0  \n",
       "23569              0.0         0.0         0.0  \n",
       "23570              0.0         0.0         0.0  \n",
       "\n",
       "[23570 rows x 18 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新老、活跃、回流用户分析\n",
    "print(df)\n",
    "pivoted_counts = df.pivot_table(\n",
    "    index='user_id',\n",
    "    columns='year_month',\n",
    "    values='order_dt',\n",
    "    aggfunc='count'\n",
    ").fillna(0)\n",
    "pivoted_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "abc9fd08-0bcd-4264-89c4-979a0a3a4389",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1997-02-01</th>\n",
       "      <th>1997-03-01</th>\n",
       "      <th>1997-04-01</th>\n",
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       "      <th>1998-05-01</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "user_id                                                                  \n",
       "1                    1           0           0           0           0   \n",
       "2                    1           0           0           0           0   \n",
       "3                    1           0           1           1           0   \n",
       "4                    1           0           0           0           0   \n",
       "5                    1           1           0           1           1   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "user_id                                                                  \n",
       "1                    0           0           0           0           0   \n",
       "2                    0           0           0           0           0   \n",
       "3                    0           0           0           0           0   \n",
       "4                    0           0           1           0           0   \n",
       "5                    1           1           0           1           0   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "user_id                                                                  \n",
       "1                    0           0           0           0           0   \n",
       "2                    0           0           0           0           0   \n",
       "3                    1           0           0           0           0   \n",
       "4                    0           1           0           0           0   \n",
       "5                    0           1           1           0           0   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "user_id                                         \n",
       "1                    0           0           0  \n",
       "2                    0           0           0  \n",
       "3                    0           1           0  \n",
       "4                    0           0           0  \n",
       "5                    0           0           0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase = pivoted_counts.map(lambda x: 1 if x>0 else 0 )  # applymap(新版本不推荐)  map 作用于数据中的每一个元素\n",
    "\n",
    "\n",
    "df_purchase.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "184696ab-01e9-4ffe-b117-5e0a74bae689",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "year_month 1997-01-01 1997-02-01 1997-03-01 1997-04-01 1997-05-01 1997-06-01  \\\n",
       "user_id                                                                        \n",
       "1                 new   unactive   unactive   unactive   unactive   unactive   \n",
       "2                 new   unactive   unactive   unactive   unactive   unactive   \n",
       "3                 new   unactive     return     active   unactive   unactive   \n",
       "4                 new   unactive   unactive   unactive   unactive   unactive   \n",
       "5                 new     active   unactive     return     active     active   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "23566           unreg      unreg        new   unactive   unactive   unactive   \n",
       "23567           unreg      unreg        new   unactive   unactive   unactive   \n",
       "23568           unreg      unreg        new     active   unactive   unactive   \n",
       "23569           unreg      unreg        new   unactive   unactive   unactive   \n",
       "23570           unreg      unreg        new   unactive   unactive   unactive   \n",
       "\n",
       "year_month 1997-07-01 1997-08-01 1997-09-01 1997-10-01 1997-11-01 1997-12-01  \\\n",
       "user_id                                                                        \n",
       "1            unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "2            unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "3            unactive   unactive   unactive   unactive     return   unactive   \n",
       "4            unactive     return   unactive   unactive   unactive     return   \n",
       "5              active   unactive     return   unactive   unactive     return   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "23566        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "23567        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "23568        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "23569        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "23570        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "\n",
       "year_month 1998-01-01 1998-02-01 1998-03-01 1998-04-01 1998-05-01 1998-06-01  \n",
       "user_id                                                                       \n",
       "1            unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "2            unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "3            unactive   unactive   unactive   unactive     return   unactive  \n",
       "4            unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "5              active   unactive   unactive   unactive   unactive   unactive  \n",
       "...               ...        ...        ...        ...        ...        ...  \n",
       "23566        unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "23567        unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "23568        unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "23569        unactive   unactive   unactive   unactive   unactive   unactive  \n",
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       "\n",
       "[23570 rows x 18 columns]"
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     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断是否是新用户、活跃用户、不活跃用户、回流用户\n",
    "def active_status(data):\n",
    "    #print(len(data))\n",
    "    status = [] #负责存储18个月的状态  unreg|new|active|unactive|return|\n",
    "    for i in range(len(data)):\n",
    "        #本月无消费\n",
    "        if data.iloc[i] == 0:\n",
    "            if len(status) == 0: #没有任何记录\n",
    "                status.append('unreg')\n",
    "            else:\n",
    "                if status[i-1] == 'unreg': #一直未消费\n",
    "                  status.append('unreg')  \n",
    "                else:\n",
    "                  status.append('unactive')  \n",
    "        else:\n",
    "            if len(status) == 0:\n",
    "                status.append('new')\n",
    "            else:\n",
    "                if status[i-1] == 'unactive':\n",
    "                    status.append('return')\n",
    "                elif status[i-1] == 'unreg':\n",
    "                    status.append('new')\n",
    "                else:\n",
    "                    status.append('active')\n",
    "    return pd.Series(status,df_purchase.columns)\n",
    "\n",
    "purchase_status = df_purchase.apply(active_status,axis=1)\n",
    "purchase_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "076649d4-7bd2-4315-832f-fa05a115ecba",
   "metadata": {},
   "outputs": [
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       "      <td>1029.0</td>\n",
       "      <td>1060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unactive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6689.0</td>\n",
       "      <td>14046</td>\n",
       "      <td>20748.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>21231.0</td>\n",
       "      <td>21390.0</td>\n",
       "      <td>21798.0</td>\n",
       "      <td>21831.0</td>\n",
       "      <td>21731.0</td>\n",
       "      <td>21542.0</td>\n",
       "      <td>21706.0</td>\n",
       "      <td>22033.0</td>\n",
       "      <td>22019.0</td>\n",
       "      <td>21510.0</td>\n",
       "      <td>22133.0</td>\n",
       "      <td>22082.0</td>\n",
       "      <td>22064.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "active             NaN      1157.0        1681      1773.0       852.0   \n",
       "new             7846.0      8476.0        7248         NaN         NaN   \n",
       "return             NaN         NaN         595      1049.0      1362.0   \n",
       "unactive           NaN      6689.0       14046     20748.0     21356.0   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "active           747.0       746.0       604.0       528.0       532.0   \n",
       "new                NaN         NaN         NaN         NaN         NaN   \n",
       "return          1592.0      1434.0      1168.0      1211.0      1307.0   \n",
       "unactive       21231.0     21390.0     21798.0     21831.0     21731.0   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "active           624.0       632.0       512.0       472.0       571.0   \n",
       "new                NaN         NaN         NaN         NaN         NaN   \n",
       "return          1404.0      1232.0      1025.0      1079.0      1489.0   \n",
       "unactive       21542.0     21706.0     22033.0     22019.0     21510.0   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "active           518.0       459.0       446.0  \n",
       "new                NaN         NaN         NaN  \n",
       "return           919.0      1029.0      1060.0  \n",
       "unactive       22133.0     22082.0     22064.0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#NaN替换unreg\n",
    "purchase_status_ct = purchase_status.replace('unreg',np.nan).apply(lambda x:pd.Series(x).value_counts())\n",
    "purchase_status_ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "feb0ecbf-a1ef-4303-9fe2-7ac79ee65bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "purchase_status_ct.T.fillna(0).plot.area()  # T \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7095f680-83ea-4ebe-ad6c-f419ffe26ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>active</th>\n",
       "      <th>new</th>\n",
       "      <th>return</th>\n",
       "      <th>unactive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-02-01</th>\n",
       "      <td>0.070886</td>\n",
       "      <td>0.519299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.409815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-01</th>\n",
       "      <td>0.071319</td>\n",
       "      <td>0.307510</td>\n",
       "      <td>0.025244</td>\n",
       "      <td>0.595927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-04-01</th>\n",
       "      <td>0.075223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044506</td>\n",
       "      <td>0.880272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-05-01</th>\n",
       "      <td>0.036148</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057785</td>\n",
       "      <td>0.906067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-06-01</th>\n",
       "      <td>0.031693</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067543</td>\n",
       "      <td>0.900764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-07-01</th>\n",
       "      <td>0.031650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060840</td>\n",
       "      <td>0.907510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-08-01</th>\n",
       "      <td>0.025626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049555</td>\n",
       "      <td>0.924820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-01</th>\n",
       "      <td>0.022401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051379</td>\n",
       "      <td>0.926220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-10-01</th>\n",
       "      <td>0.022571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055452</td>\n",
       "      <td>0.921977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-01</th>\n",
       "      <td>0.026474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059567</td>\n",
       "      <td>0.913958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-01</th>\n",
       "      <td>0.026814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052270</td>\n",
       "      <td>0.920916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>0.021723</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043487</td>\n",
       "      <td>0.934790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-02-01</th>\n",
       "      <td>0.020025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045779</td>\n",
       "      <td>0.934196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-03-01</th>\n",
       "      <td>0.024226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.063174</td>\n",
       "      <td>0.912601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-04-01</th>\n",
       "      <td>0.021977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038990</td>\n",
       "      <td>0.939033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-05-01</th>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043657</td>\n",
       "      <td>0.936869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-06-01</th>\n",
       "      <td>0.018922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044972</td>\n",
       "      <td>0.936105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              active       new    return  unactive\n",
       "year_month                                        \n",
       "1997-01-01  0.000000  1.000000  0.000000  0.000000\n",
       "1997-02-01  0.070886  0.519299  0.000000  0.409815\n",
       "1997-03-01  0.071319  0.307510  0.025244  0.595927\n",
       "1997-04-01  0.075223  0.000000  0.044506  0.880272\n",
       "1997-05-01  0.036148  0.000000  0.057785  0.906067\n",
       "1997-06-01  0.031693  0.000000  0.067543  0.900764\n",
       "1997-07-01  0.031650  0.000000  0.060840  0.907510\n",
       "1997-08-01  0.025626  0.000000  0.049555  0.924820\n",
       "1997-09-01  0.022401  0.000000  0.051379  0.926220\n",
       "1997-10-01  0.022571  0.000000  0.055452  0.921977\n",
       "1997-11-01  0.026474  0.000000  0.059567  0.913958\n",
       "1997-12-01  0.026814  0.000000  0.052270  0.920916\n",
       "1998-01-01  0.021723  0.000000  0.043487  0.934790\n",
       "1998-02-01  0.020025  0.000000  0.045779  0.934196\n",
       "1998-03-01  0.024226  0.000000  0.063174  0.912601\n",
       "1998-04-01  0.021977  0.000000  0.038990  0.939033\n",
       "1998-05-01  0.019474  0.000000  0.043657  0.936869\n",
       "1998-06-01  0.018922  0.000000  0.044972  0.936105"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#回流用户消费占比\n",
    "purchase_status_t = purchase_status_ct.T.fillna(0)\n",
    "rate =   purchase_status_t.apply(lambda  x : x/x.sum(),axis=1)\n",
    "rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "738200ac-e6b0-4c60-96f4-532b6a31ccb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rate['return'],label='return')\n",
    "plt.plot(rate['active'],label='active')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b52d7654-1ca6-4417-915d-18031fc9b62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                         46089\n",
       "mean     68 days 23:22:13.567662566\n",
       "std      91 days 00:47:33.924168893\n",
       "min                 0 days 00:00:00\n",
       "25%                10 days 00:00:00\n",
       "50%                31 days 00:00:00\n",
       "75%                89 days 00:00:00\n",
       "max               533 days 00:00:00\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户的购买周期\n",
    "order_diff = df.groupby(by='user_id').apply(lambda x:x['date']-x['date'].shift(), include_groups=False)\n",
    "order_diff.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "afedbb57-9589-408d-92eb-068d612af7ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(order_diff/np.timedelta64(1,'D')).hist(bins=20) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2dec98a1-4eb4-4788-a21d-12c20e4c5c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用户生命周期：用户最后一次购买与第一次购买的时间差  如果差值为0 则用户仅购买的了一次\n",
    "\n",
    "user_life = df.groupby('user_id')['date'].agg(['min','max'])\n",
    "(user_life['max']==user_life['min']).value_counts().plot(kind=\"pie\",autopct='%1.1f%%')\n",
    "plt.legend(['仅消费一次','多次消费'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2e2535d3-b494-405a-a402-527557233ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                          23570\n",
       "mean     134 days 20:55:36.987696224\n",
       "std      180 days 13:46:43.039788104\n",
       "min                  0 days 00:00:00\n",
       "25%                  0 days 00:00:00\n",
       "50%                  0 days 00:00:00\n",
       "75%                294 days 00:00:00\n",
       "max                544 days 00:00:00\n",
       "dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user_life['max']-user_life['min']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e997875b-e88d-49be-aed1-1f2df61e47ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制生命周期直方\n",
    "plt.figure(figsize= (12,6))\n",
    "plt.subplot(121)\n",
    "((user_life['max']-user_life['min'])/np.timedelta64(1,'D')).hist(bins=15)\n",
    "plt.title('histgram of user life')\n",
    "plt.xlabel('user life /days')\n",
    "plt.ylabel('number of user ')\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "user_period = (user_life['max']-user_life['min']).reset_index()[0]/np.timedelta64(1,'D')\n",
    "(user_period[user_period > 0 ]).hist(bins=15)\n",
    "plt.title('histgram of multiple consumption user life ')\n",
    "plt.xlabel('user life /days')\n",
    "plt.ylabel('number of user ')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "53bd9c0e-a164-402d-8285-111511f1cea4",
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1997-02-01</th>\n",
       "      <th>1997-03-01</th>\n",
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       "<p>23570 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "user_id                                                                  \n",
       "1                  0.0         NaN         NaN         NaN         NaN   \n",
       "2                  1.0         NaN         NaN         NaN         NaN   \n",
       "3                  0.0         NaN         0.0         0.0         NaN   \n",
       "4                  1.0         NaN         NaN         NaN         NaN   \n",
       "5                  1.0         0.0         NaN         0.0         0.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         0.0         NaN         NaN   \n",
       "23567              NaN         NaN         0.0         NaN         NaN   \n",
       "23568              NaN         NaN         0.0         1.0         NaN   \n",
       "23569              NaN         NaN         0.0         NaN         NaN   \n",
       "23570              NaN         NaN         1.0         NaN         NaN   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "user_id                                                                  \n",
       "1                  NaN         NaN         NaN         NaN         NaN   \n",
       "2                  NaN         NaN         NaN         NaN         NaN   \n",
       "3                  NaN         NaN         NaN         NaN         NaN   \n",
       "4                  NaN         NaN         0.0         NaN         NaN   \n",
       "5                  0.0         0.0         NaN         0.0         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         NaN         NaN         NaN   \n",
       "23567              NaN         NaN         NaN         NaN         NaN   \n",
       "23568              NaN         NaN         NaN         NaN         NaN   \n",
       "23569              NaN         NaN         NaN         NaN         NaN   \n",
       "23570              NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "user_id                                                                  \n",
       "1                  NaN         NaN         NaN         NaN         NaN   \n",
       "2                  NaN         NaN         NaN         NaN         NaN   \n",
       "3                  1.0         NaN         NaN         NaN         NaN   \n",
       "4                  NaN         0.0         NaN         NaN         NaN   \n",
       "5                  NaN         1.0         0.0         NaN         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         NaN         NaN         NaN   \n",
       "23567              NaN         NaN         NaN         NaN         NaN   \n",
       "23568              NaN         NaN         NaN         NaN         NaN   \n",
       "23569              NaN         NaN         NaN         NaN         NaN   \n",
       "23570              NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "user_id                                         \n",
       "1                  NaN         NaN         NaN  \n",
       "2                  NaN         NaN         NaN  \n",
       "3                  NaN         0.0         NaN  \n",
       "4                  NaN         NaN         NaN  \n",
       "5                  NaN         NaN         NaN  \n",
       "...                ...         ...         ...  \n",
       "23566              NaN         NaN         NaN  \n",
       "23567              NaN         NaN         NaN  \n",
       "23568              NaN         NaN         NaN  \n",
       "23569              NaN         NaN         NaN  \n",
       "23570              NaN         NaN         NaN  \n",
       "\n",
       "[23570 rows x 18 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#复购率分析   在自然月内，多次购买用户在总消费人数中的占比\n",
    "\n",
    "pivoted_repurchase = pivoted_counts.map(lambda x: 1 if x>1 else np.nan if x==0 else 0)\n",
    "pivoted_repurchase\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3631cbec-7491-486f-93bb-eeae5bdc98ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "year_month\n",
      "1997-01-01    0.107571\n",
      "1997-02-01    0.122288\n",
      "1997-03-01    0.155292\n",
      "1997-04-01    0.223600\n",
      "1997-05-01    0.196929\n",
      "1997-06-01    0.195810\n",
      "1997-07-01    0.215138\n",
      "1997-08-01    0.200339\n",
      "1997-09-01    0.202415\n",
      "1997-10-01    0.206634\n",
      "1997-11-01    0.202170\n",
      "1997-12-01    0.219957\n",
      "1998-01-01    0.210800\n",
      "1998-02-01    0.203095\n",
      "1998-03-01    0.229612\n",
      "1998-04-01    0.199026\n",
      "1998-05-01    0.200269\n",
      "1998-06-01    0.214475\n",
      "dtype: float64\n",
      "year_month\n",
      "1997-01-01    0.107571\n",
      "1997-02-01    0.122288\n",
      "1997-03-01    0.155292\n",
      "1997-04-01    0.223600\n",
      "1997-05-01    0.196929\n",
      "1997-06-01    0.195810\n",
      "1997-07-01    0.215138\n",
      "1997-08-01    0.200339\n",
      "1997-09-01    0.202415\n",
      "1997-10-01    0.206634\n",
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      "1997-12-01    0.219957\n",
      "1998-01-01    0.210800\n",
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      "1998-03-01    0.229612\n",
      "1998-04-01    0.199026\n",
      "1998-05-01    0.200269\n",
      "1998-06-01    0.214475\n",
      "dtype: float64\n"
     ]
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nan 不参与count计数\n",
    "print(pivoted_repurchase.sum()/pivoted_repurchase.count()) #方案1\n",
    "print(pivoted_repurchase.apply(lambda x:x.sum()/x.count())) #方案2\n",
    "\n",
    "\n",
    "pivoted_repurchase.apply(lambda x:x.sum()/x.count()).plot(figsize=(12,6))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c68e7001-572d-477b-b24c-d7b4223e1dd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
      "user_id                                                                  \n",
      "1                    1           0           0           0           0   \n",
      "2                    1           0           0           0           0   \n",
      "3                    1           0           1           1           0   \n",
      "4                    1           0           0           0           0   \n",
      "5                    1           1           0           1           1   \n",
      "...                ...         ...         ...         ...         ...   \n",
      "23566                0           0           1           0           0   \n",
      "23567                0           0           1           0           0   \n",
      "23568                0           0           1           1           0   \n",
      "23569                0           0           1           0           0   \n",
      "23570                0           0           1           0           0   \n",
      "\n",
      "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
      "user_id                                                                  \n",
      "1                    0           0           0           0           0   \n",
      "2                    0           0           0           0           0   \n",
      "3                    0           0           0           0           0   \n",
      "4                    0           0           1           0           0   \n",
      "5                    1           1           0           1           0   \n",
      "...                ...         ...         ...         ...         ...   \n",
      "23566                0           0           0           0           0   \n",
      "23567                0           0           0           0           0   \n",
      "23568                0           0           0           0           0   \n",
      "23569                0           0           0           0           0   \n",
      "23570                0           0           0           0           0   \n",
      "\n",
      "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
      "user_id                                                                  \n",
      "1                    0           0           0           0           0   \n",
      "2                    0           0           0           0           0   \n",
      "3                    1           0           0           0           0   \n",
      "4                    0           1           0           0           0   \n",
      "5                    0           1           1           0           0   \n",
      "...                ...         ...         ...         ...         ...   \n",
      "23566                0           0           0           0           0   \n",
      "23567                0           0           0           0           0   \n",
      "23568                0           0           0           0           0   \n",
      "23569                0           0           0           0           0   \n",
      "23570                0           0           0           0           0   \n",
      "\n",
      "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
      "user_id                                         \n",
      "1                    0           0           0  \n",
      "2                    0           0           0  \n",
      "3                    0           1           0  \n",
      "4                    0           0           0  \n",
      "5                    0           0           0  \n",
      "...                ...         ...         ...  \n",
      "23566                0           0           0  \n",
      "23567                0           0           0  \n",
      "23568                0           0           0  \n",
      "23569                0           0           0  \n",
      "23570                0           0           0  \n",
      "\n",
      "[23570 rows x 18 columns]\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1997-02-01</th>\n",
       "      <th>1997-03-01</th>\n",
       "      <th>1997-04-01</th>\n",
       "      <th>1997-05-01</th>\n",
       "      <th>1997-06-01</th>\n",
       "      <th>1997-07-01</th>\n",
       "      <th>1997-08-01</th>\n",
       "      <th>1997-09-01</th>\n",
       "      <th>1997-10-01</th>\n",
       "      <th>1997-11-01</th>\n",
       "      <th>1997-12-01</th>\n",
       "      <th>1998-01-01</th>\n",
       "      <th>1998-02-01</th>\n",
       "      <th>1998-03-01</th>\n",
       "      <th>1998-04-01</th>\n",
       "      <th>1998-05-01</th>\n",
       "      <th>1998-06-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "user_id                                                                  \n",
       "1                  0.0         NaN         NaN         NaN         NaN   \n",
       "2                  0.0         NaN         NaN         NaN         NaN   \n",
       "3                  0.0         NaN         0.0         1.0         NaN   \n",
       "4                  0.0         NaN         NaN         NaN         NaN   \n",
       "5                  0.0         1.0         NaN         0.0         1.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         0.0         NaN         NaN   \n",
       "23567              NaN         NaN         0.0         NaN         NaN   \n",
       "23568              NaN         NaN         0.0         1.0         NaN   \n",
       "23569              NaN         NaN         0.0         NaN         NaN   \n",
       "23570              NaN         NaN         0.0         NaN         NaN   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "user_id                                                                  \n",
       "1                  NaN         NaN         NaN         NaN         NaN   \n",
       "2                  NaN         NaN         NaN         NaN         NaN   \n",
       "3                  NaN         NaN         NaN         NaN         NaN   \n",
       "4                  NaN         NaN         0.0         NaN         NaN   \n",
       "5                  1.0         1.0         NaN         0.0         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         NaN         NaN         NaN   \n",
       "23567              NaN         NaN         NaN         NaN         NaN   \n",
       "23568              NaN         NaN         NaN         NaN         NaN   \n",
       "23569              NaN         NaN         NaN         NaN         NaN   \n",
       "23570              NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "user_id                                                                  \n",
       "1                  NaN         NaN         NaN         NaN         NaN   \n",
       "2                  NaN         NaN         NaN         NaN         NaN   \n",
       "3                  0.0         NaN         NaN         NaN         NaN   \n",
       "4                  NaN         0.0         NaN         NaN         NaN   \n",
       "5                  NaN         0.0         1.0         NaN         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         NaN         NaN         NaN   \n",
       "23567              NaN         NaN         NaN         NaN         NaN   \n",
       "23568              NaN         NaN         NaN         NaN         NaN   \n",
       "23569              NaN         NaN         NaN         NaN         NaN   \n",
       "23570              NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "user_id                                         \n",
       "1                  NaN         NaN         NaN  \n",
       "2                  NaN         NaN         NaN  \n",
       "3                  NaN         0.0         NaN  \n",
       "4                  NaN         NaN         NaN  \n",
       "5                  NaN         NaN         NaN  \n",
       "...                ...         ...         ...  \n",
       "23566              NaN         NaN         NaN  \n",
       "23567              NaN         NaN         NaN  \n",
       "23568              NaN         NaN         NaN  \n",
       "23569              NaN         NaN         NaN  \n",
       "23570              NaN         NaN         NaN  \n",
       "\n",
       "[23570 rows x 18 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#回购率分析  上月购买了，本月继续购买\n",
    "print(df_purchase)\n",
    "def purchase_back(data):\n",
    "    status = []\n",
    "    status.append(np.nan if data.iloc[0] == 0 else 0) #填充第一个数据，补齐列数\n",
    "    for i in range(len(data) -1):\n",
    "        if data.iloc[i+1] ==1: \n",
    "            if data.iloc[i] == 1:\n",
    "                status.append(1)\n",
    "            else:\n",
    "                status.append(0)\n",
    "        else:\n",
    "            status.append(np.nan)\n",
    "    return pd.Series(status,df_purchase.columns)\n",
    "\n",
    "purchase_b = df_purchase.apply(purchase_back,axis=1)\n",
    "purchase_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "24f2303c-9e9b-4528-9828-fe3f005ab980",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "plt.subplot(211)\n",
    "#回购率\n",
    "(purchase_b.sum()/purchase_b.count()).plot(label='回购率')  #label设置标签，主要用于提供图例显示\n",
    "#复购率\n",
    "pivoted_repurchase.apply(lambda x:x.sum()/x.count()).plot(label='复购率')\n",
    "plt.legend()\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('占比')\n",
    "\n",
    "#回购人数与购物总人数\n",
    "plt.subplot(212)\n",
    "purchase_b.sum().plot(label='回购人数')\n",
    "purchase_b.count().plot(label='购物总人数')\n",
    "plt.legend()\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('人数')\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cc23f11-49be-494e-a19b-f433f528d915",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ai_training",
   "language": "python",
   "name": "ai-training"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
